Multi-framework ASR comparison platform for evaluating acoustic modeling performance across high and low-resource languages
# Multi-Framework ASR Comparison Platform
A comprehensive research platform for comparing Automatic Speech Recognition (ASR) frameworks across multiple languages, with emphasis on low-resource language acoustic modeling performance.
## 🎯 Research Objective
Investigate whether transformer-based ASR models (Whisper) maintain superiority over CTC-based models (Wav2Vec2) when applied to low-resource languages with limited training data.
## 🚀 Features
- **Multi-Framework Support**: Compare Whisper, Wav2Vec2, and SpeechBrain
- **Multilingual Testing**: 10+ languages including low-resource varieties
- **Comprehensive Metrics**: WER, Verb/Noun Ratio, Average Utterance Length, Real-Time Factor
- **Video Support**: Automatic audio extraction from MP4, AVI, MOV, MKV, WebM
- **Professional UI**: Streamlit-based interface with visualization dashboards
- **Data Export**: Results exportable as CSV/JSON for analysis
## 📦 Supported Languages
**High-Resource:**
- English, Spanish, Portuguese, Mandarin Chinese, French, German
**Low-Resource:**
- Haitian Creole, Creole variants
**Medium-Resource:**
- Italian, Russian, Arabic, Japanese
## 🛠️ Installation
### Prerequisites
- Python 3.11
- macOS, Linux, or Windows
### Setup
```bash
# Clone repository
git clone
github.com
cd asr-framework-comparison
# Create virtual environment
python3.11 -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install --upgrade pip
pip install -r requirements.txt
# Download spaCy model
python -m spacy download en_core_web_sm
```
## 🎬 Quick Start
```bash
# Activate virtual environment
source venv/bin/activate
# Run the application
streamlit run app.py
```
The app will open in your browser at `
localhost`
## 📊 Usage
1. **Select Language**: Choose from 10+ supported languages
2. **Load Models**: Click buttons to load Whisper, Wav2Vec2, SpeechBrain
3. **Upload Audio/Video**: Drag and drop …